Transcript Slide 1

Subprime Story
Attila Agod
Morgan Stanley Hungary Analytics Ltd.
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The following 57 minutes
• Recent history of US housing market
• Housing derivatives
• The collapse
• Effect on financial sector
• Gaussian copula model
• Q&A
Scales
• Moldova GDP
$4B
• Morgan Stanley capitalization
$26B
• Hungarian GDP
$138B
• Yearly wheat consumption ($400/t)$250B
• Subprime related write-downs
$504B
• Yearly oil consumption ($50/bbl)
$1600B
• War in Iraq (Congress
• USA GDP
Stiglitz) $646B - $3000B
$13800B
Housing market
• Residential real estate value (2006):
$22.400B
• Top 2000 companies (2008):
$39.000B
• International bond market (2006):
$45.000B
• 9.31% return with only 2.77% volatility (1997–2007)
• 31.5% of purchases were pure investment (2006)
asset
return
volatility
housing
9.31%
2.77%
bonds
5.97%
3.47%
stocks
5.91%
14.72%
REITs
11.22%
15.22%
Mortgage market
• Steadily increasing originations
• Refinancing waves
• Cash-out refinancing is popular
• People ate up the price increase
• What have been produced?
Subprime mortgages
• Does not meet Fannie Mae or Freddie Mac guidelines
• Lower credit rating, FICO < 620
• 7.5 million mortgages (1 of 5 in 2006)
• $1300B (March 2007)
• Decreasing risk premium:
280 bps (2001)
130 bps (2007)
• Frauds
- predatory lending
• NINJA borrowers
30000
25000
20000
15000
10000
5000
0
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
- predatory borrowing
Reported mortgage frauds
Housing derivatives
• Borrower – servicer – investment bank – investors
• Securities backed by ~1000 mortgages
• E.g.: Senior bonds receive all principal for 3 years. Then all bonds receive pro rata.
• Credit enhancement by over-collateralization
15%
Prepay Penalties
Servicing
Available Interest
7%
Subordinated Excess
L+700
L+325
L+270
L+195
L+160
L+65
L+37
Principal
80.50%
AAA
2.8-Yr
A/L
6.40%
AA
5.3-Yr
5.25%
A
5.3-Yr
1.75%
A5.3-Yr
1.50%
BBB
5.3-Yr
1.05%
BBB
5.3-Yr
1.30%
BBB5.3-Yr
2.25%
OC
5.88%
BBB
NIM
0.69-Yr
1.109%
NR
Equity
2.36-Yr
The collapse - derivatives
• Junior tranches are wiped out
• Credit rating agencies downgraded CDOs and MBSs
• Forced asset sales
downward pressure on prices
• Cleaned up balance sheets
ABX 06-2 indices
120
AAA
100
AA
A
prices
80
BBB
60
40
20
0
24/03/2006
10/10/2006
28/04/2007
14/11/2007
date
01/06/2008
18/12/2008
06/07/2009
Losses of financial institutions
Citigroup
$55.1B
Merrill Lynch
$51.8B
UBS
$44.2B
HSBC
$27.4B
Wachovia
$22.5B
Bank of America
$21.2B
IKB Deutsche
$15.3B
Royal Bank of Scotland
$14.9B
Washington Mutual
$14.8B
Morgan Stanley
$14.4B
JPMorgan Chase
$14.3B
Deutsche Bank
$10.8B
Credit crunch
• LIBOR - OIS
• Increased volatility
• Huge bid-ask spreads
• Decreasing stock prices
• Increased country risk
Pricing problem – a toy model
• Given
- single names, N
- attachment and detachment points, A and D
- maturity, T
• Assume
- identical balances, B
- constant hazard rate, l
- constant recovery, R
• Ignore
- interest rates
- principal payback
- triggers
• Estimate
- tranche prices
- deltas
collateral
pool
A
D
loss
distribution
Naïve approach
l  exp lt , t  0
f t   
t 0
0,
• N = 100
• B = $1.0
• l = 0.1 / year
0.1
• R = 0.0
0.06
f(t)
• T = 1.0 year
0.08
0.04
• [A ; D] : [0.0 : 0.1], [0.2 : 0.3], [0.4 : 0.5]
0.02
• Monte Carlo method
- 2e5 paths (scenarios)
- draw N default times, Tdef
- if Tdef < T, then write down B
0
10
20
30
40
50
0.6
0.8
1
time
0.2
0.15
probability
• Prices
- [0.0 : 0.1] : $1.42
- [0.2 : 0.3] : $9.9994
- [0.4 : 0.5] : $10.0
0
0.1
0.05
0
0
0.2
0.4
loss
Gaussian copula
• Correlate default events
1
Z i    M  1   2 Wi
• M: macroeconomic factors
CDF(x)
0.8
• Wi: idiosyncratic risk source
0.6
0.4
0.2
• The most dangerous part is
when people believe everything
coming out of it. (David X. Li)
0
-4
-2
0
2
4
6
x
8
10
12
14
16
correlation
10
10
0
0
0.11
0.22
0.33
0.44
0.55
0.66
0.77
0.88
0.99
8
probability
price
10
6
4
2
0
10
0.0 - 0.1
0.2 - 0.3
0.4 - 0.5
0
0.2
0.4
0.6
correlation
0.8
1
10
-2
-4
-6
0
0.2
0.4
0.6
loss
0.8
1
The nine lives of a senior tranche (JPM)
• If the cat has one life he prefers clusters (at least there are some paths between…)
• If the cat has nine lives he prefers evenly scattered traps (and avoiding clusters…)
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